Linear mixed models were adjusted for age at the time of ART initiation, gender, year of starting ART and clinical stage at starting ART.
We considered the following important confounders: age and overall company tenure at job initiation, gender, race, physical demand of the job, union status of the plant, plant type (e.g., whether it included a smelting facility), and socio-demographic characteristics.
Cox's models for discrete time survival analyses, stratified by school and age group were used to estimate the risk of drug use in relation to age of alcohol and tobacco use initiation by gender, while accommodating the complex survey design.
We fitted a random-intercept linear mixed model for repeated measures using all available CD4 measurements at each time point to explore the effect of several characteristics of patients at HAART initiation: age, gender, Body mass index (BMI), WHO clinical stage, CD4 cell count and haemoglobin level.
Factors significantly associated with shorter time to treatment initiation included female gender and use of cardiovascular medications at baseline or initiated during follow up.
Greater increases among females resulted in attenuation, and for early sexual initiation, removal, of gender differences.
Clinical stage IV, hemoglobin ≤10 g/dL, and CPT initiation (but not gender) were strongly related to mortality.
Other significant predictors associated with shorter time to antihyperglycaemic medication initiation included female gender, use of lipid-modifying agents, use of weight-reducing agents and later physician registration year.
During the first year after initiation of HAART, gender had no impact on risk of first modification of treatment due to either toxicity (adjusted incidence rate ratio women vs. men, IRR, 0.97 95% confidence interval (CI) 0.66-1.44) other/unknownown reasons (adjusted IRR women vs. men 1.18 95% CI, 0.76-1.85) (Table 2).
Covariates included age at initiation of dialysis, gender, race (AA vs. non-AA), diabetes as a co-morbid condition, dialysis modality (for at least 60 days, from the treatment history files), body mass index, serum albumin and other co-morbid conditions from the Medical Evidence Form known to be associated with the outcomes, particularly prior history of stroke.
Further analysis was conducted to assess whether the following variables might moderate the relationship between technology acceptance and initiation of use: gender, age group, employment, hours of care per week, years of care, age at immigration, language preference, living with care recipient, positive aspects of caregiving, social support, and technology aptitude.
